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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Trends in Carbon, Oxygen, and Nitrogen Core in the X-ray Absorption Spectroscopy of Carbon Nanomaterials: A Guide for the Perplexed

Successful deployment of carbon nanomaterials in many applications, such as sensing, energy storage, and catalysis, relies on the selection, synthesis, and tailoring of the surface properties. Predictive analysis of the behavior is difficult without detailed knowledge of the differences between various carbon nanomaterials and their surface functionalization, thus leaving the selection process to traditional trial-and-error work. The present characterization fills this knowledge gap for carbon nanomaterial surface properties with respect to chemical states and functionalization. We present an overview of the chemical trends that can be extracted from soft X-ray absorption spectroscopy (XAS) spectra on an extended set of nonideal carbon nanomaterials as a function of sp 2 bonded carbon and bond ordering. In particular, the surface chemical state, the presence of long-range order in the carbon matrix, and a qualitative estimation of the amount of oxygen and nitrogen and their respective functional group formation on the material surface, together with the detailed material fabrication parameters, are reported. The results expand our understanding of carbon nanomaterial functionalization, which can support material selection in practice, provided that the specifications of the application are known.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Insights into Plastic Localization by Crystallographic Slip from Emerging Experimental and Numerical Approaches

Advanced experimental and numerical approaches are being developed to capture the localization of plasticity at the nanometer scale as a function of the multiscale and heterogeneous microstructure present in metallic materials. These innovative approaches promise new avenues to understand microstructural effects on mechanical properties, accelerate alloy design, and enable more accurate mechanical property prediction. This article provides an overview of emerging approaches with a focus on the localization of plasticity by crystallographic slip. New insights into the mechanisms and mechanics of strain localization are addressed. The consequences of the localization of plasticity by deformation slip for mechanical properties of metallic materials are also detailed.

36 MATERIALS SCIENCE↗
NASA NTRS2004

Predicting the Influence of Nano-Scale Material Structure on the In-Plane Buckling of Orthotropic Plates

A multi-scale analysis of the structural stability of a carbon nanotube-polymer composite material is developed. The influence of intrinsic molecular structure, such as nanotube length, volume fraction, orientation and chemical functionalization, is investigated by assessing the relative change in critical, in-plane buckling loads. The analysis method relies on elastic properties predicted using the hierarchical, constitutive equations developed from the equivalent-continuum modeling technique applied to the buckling analysis of an orthotropic plate. The results indicate that for the specific composite materials considered in this study, a composite with randomly orientated carbon nanotubes consistently provides the highest values of critical buckling load and that for low volume fraction composites, the non-functionalized nanotube material provides an increase in critical buckling stability with respect to the functionalized system.

Gates, Thomas S.↗
DOE OSTI2025

Latent space mapping: Revolutionizing predictive models for divertor plasma detachment control

The inherent complexity of boundary plasma, characterized by multi-scale and multi-physics challenges, has historically restricted high-fidelity simulations to scientific research due to their intensive computational demands. Consequently, routine applications such as discharge control and scenario development have relied on faster but less accurate empirical methods. This work introduces DivControlNN, a novel machine-learning-based surrogate model designed to address these limitations by enabling quasi-real-time predictions (i.e., ~ 0.2 ms) of boundary and divertor plasma behavior. Trained on over 70,000 2D UEDGE simulations from KSTAR tokamak equilibria, DivControlNN employs latent space mapping to efficiently represent complex divertor plasma states, achieving a computational speed-up of over 10 8 compared to traditional simulations while maintaining a relative error below 20% for key plasma property predictions. During the 2024 KSTAR experimental campaign, a prototype detachment control system powered by DivControlNN successfully demonstrated detachment control on its first attempt, even for a new tungsten divertor configuration and without any fine-tuning. These results highlight the transformative potential of DivControlNN in overcoming diagnostic challenges in future fusion reactors by providing fast, robust, and reliable predictions for advanced integrated control systems.

Artificial neural networks↗
DOE OSTI2025

Advancing molecular machine learning representations with stereoelectronics-infused molecular graphs

Molecular representation is a critical element in our understanding of the physical world and the foundation for modern molecular machine learning. Previous molecular machine learning models have used strings, fingerprints, global features and simple molecular graphs that are inherently information-sparse representations. However, as the complexity of prediction tasks increases, the molecular representation needs to encode higher fidelity information. This work introduces a new approach to infusing quantum-chemical-rich information into molecular graphs via stereoelectronic effects, enhancing expressivity and interpretability. Learning to predict the stereoelectronics-infused representation with a tailored double graph neural network workflow enables its application to any downstream molecular machine learning task without expensive quantum-chemical calculations. We show that the explicit addition of stereoelectronic information substantially improves the performance of message-passing two-dimensional machine learning models for molecular property prediction. We show that the learned representations trained on small molecules can accurately extrapolate to much larger molecular structures, yielding chemical insight into orbital interactions for previously intractable systems, such as entire proteins, opening new avenues of molecular design. Finally, we have developed a web application (simg.cheme.cmu.edu) where users can rapidly explore stereoelectronic information for their own molecular systems.

Boiko, Daniil A↗
NASA NTRS1993

Rocket plume base heating methodology

A review of radiative transport calculation methods for base heating is presented followed by a description of the current methodology for the Space Shuttle plume radiation predictions and improvements for the Advanced Solid Rocket Booster (ASRB). The calculation methods include empirical methods, the SIRRM code and the forward and reverse Monte Carlo methods. Current plume radiation methods include those used for the Space Shuttle Main Engines and the Solid Rocket Booster (SRB). Methods being developed for the ASRB include changes in plume property prediction methodology and application of the reverse Monte Carlo method in predicting plume radiation models. Results of the prediction methods are compared with experimental measurements on the current SRB and on 1/6-scale motors using both SRB and ASRB propellants. Examples are also presented demonstrating the statistical results available with the reverse Monte Carlo method.

Reardon, John E.↗
NASA NTRS1995

Electronic characterization of defects in narrow gap semiconductors: Comparison of electronic energy levels and formation energies in mercury cadmium telluride, mercury zinc telluride, and mercury zinc selenide

The project has evolved to that of using Green's functions to predict properties of deep defects in narrow gap materials. Deep defects are now defined as originating from short range potentials and are often located near the middle of the energy gap. They are important because they affect the lifetime of charge carriers and hence the switching time of transistors. We are now moving into the arena of predicting formation energies of deep defects. This will also allow us to make predictions about the relative concentrations of the defects that could be expected at a given temperature. The narrow gap materials mercury cadmium telluride (MCT), mercury zinc telluride (MZT), and mercury zinc selenide (MZS) are of interest to NASA because they have commercial value for infrared detecting materials, and because there is a good possibility that they can be grown better in a microgravity environment. The uniform growth of these crystals on earth is difficult because of convection (caused by solute depletion just ahead of the growing interface, and also due to thermal gradients). In general it is very difficult to grow crystals with both radial and axial homogeneity.

Patterson, James D.↗
NASA NTRS2005

High LMD GCM Resolution Modeling of the Seasonal Evolution of the Martian Northern Permanent Cap: Comparison with Mars Express OMEGA Observations

Analyses of imaging data from Mariner, Viking and MGS have shown that surface properties (albedo, temperature) of the northern cap present significant differences within the summer season and between Mars years. These observations include differential brightening and/or darkening between polar areas from the end of the spring to midsummer. These differences are attributed to changes in grain size or dust content of surface ice. To better understand the summer behavior of the permanent northern polar cap, we perfomed a high resolution modeling (approximately 1 deg x 1 deg.) of northern cap in the Martian Climate/water cycle as simulated by the Laboratoire de Meteorologie Dynamique (LMD) global climate model. We compare the predicted properties of the surface ice (ice thickness, temperature) with the Mars Express Omega summer observations of the northern cap. albedo and thermal inertia svariations model. In particular, albedo variations could be constrained by OMEGA data. Meteorological predictions of the LMD GCM wil be presented at the conference to interpret the unprecedently resolved OMEGA observations. The specific evolution of regions of interest (cap center, Chasma Boreal...) and the possibility of late summer global cap brightening will be discussed.

Levrard, B.↗
NASA NTRS1992

Design and analysis of filament-wound cylinders in compression

The objective of the study was to develop a more accurate model for the prediction of the compressive behavior of filament-wound composite cylindrical shells. Results from a closed-form analytical model and a three-dimensional finite element model show that the extensional and bending stiffness of the laminate is significantly reduced in tow cross-over regions and that bending-extension coupling is increased in comparison with classical lamination theory. The material properties predicted by a micromechanics analysis are included in a global finite element model of a filament-wound circular cylindrical shell which also incorporates variations in the laminate stacking thickness. Results from the global shell analysis are compared with experimental data.

Hipp, Patrick A.↗
DOE OSTI2023

Evolution of artificial intelligence for application in contemporary materials science

Abstract Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract

Materials Science↗
DOE OSTI2021

Autonomous nondestructive evaluation of resistance spot welded joints

The application of non-destructive evaluation approaches has attracted strong interests in modern automotive industries. Here, we present an autonomous deep-computing framework to analyze raw videos from infrared systems and to predict weld nugget shape and size with unprecedented accuracy and speed. In a comprehensive training and testing experiment with 90 videos (seven sets of welding material stack-ups), a new method was developed to assemble sufficient datasets for neural network training. Our framework successfully predicts all the nugget shapes with F1 scores that range from 0.84 to 0.92. The total training time on Nvidia DGX station takes less than 10 min for each set of welding material stack-up. The real inference time of an individual dataset (with 30 video frames) takes about 0.005 s. The procedure and methods developed in the study can be applied to other image-based weld property prediction, as well as other manufacturing processes. Furthermore, our well-trained neural networks take limited memory resources (2.3 MB) and are suitable for embedded microprocessors for in-situ welding quality control as edge computing within an intelligent welding framework.

42 ENGINEERING↗
DOE OSTI2026

HydraGNN_GFM_FineTuning4Materials v1.0

This repository enables fine-tuning of the HydraGNN Predictive GFM 2026 — an open-source ensemble of pre-trained graph foundation models for atomistic materials modeling, developed at Oak Ridge National Laboratory. The GFM 2026 is freely available and downloadable via Globus from the OLCF Data Constellation (DOI: 10.13139/OLCF/2562660). Starting from these pre-trained weights, this repository provides a complete transfer learning pipeline for adapting the GFM ensemble to domain-specific molecular and materials property prediction tasks. It includes: 1) Utilities for ensemble fine-tuning with task-specific output heads 2) Example pipelines for eight widely-used materials and molecular datasets 3) Tools for model adaptation and head configuration 4) Data preprocessing utilities for each supported dataset 5) Benchmarking and evaluation scripts

Ungerboeck, Linda↗
NASA NTRS1980

Concepts and techniques for ultrasonic evaluation of material mechanical properties

The ultrasonic nondestructive evaluation techniques discussed in the present paper indicate potentials for material characterization and property prediction. Stress wave interaction and material transfer function concepts are examined as a basis for explaining correlations between material mechanical behavior and ultrasonically measured quantities. It is observed that the effect and criticality of any discrete flaw, such as crack, inclusion, or any other stress raiser, is definable only in terms of its material microstructural environment. This underscores the importance of ultrasonic techniques capable of characterizing the stress wave energy transfer properties of a material.

Vary, A.↗
DOE OSTI2025

The Path towards Plasma Facing Components: A Review of State-of-the-art in W-Based Refractory High-Entropy Alloys

Developing advanced materials for plasma-facing components (PFCs) in fusion reactors is a crucial aspect for achieving sustained energy production. Tungsten (W) - based refractory high-entropy alloys (RHEAs) have emerged as promising candidates due to their superior radiation tolerance and high-temperature strength. This review paper will focus on recent advancements in W-based RHEA research, particularly emphasizing the key role of modelling using machine learning (ML) in the stage of discovery by predicting properties for each composition and expediting the identification of optimal RHEA compositions with desired properties. Additionally, the application of additive manufacturing (AM) techniques for fabricating W-based RHEAs is explored, highlighting their advantages for rapid prototyping and multi-compositional sample production in a high throughput manner. The review critically evaluates the current understanding of mechanical properties relevant to PFC applications, including high-temperature strength and ductility. Furthermore, the radiation tolerance of W-based RHEAs under irradiated conditions is discussed. Finally, the validity of current AM-manufactured W-based RHEAs as PFC materials is assessed, and key challenges and opportunities for future research are identified. This review aims to provide a comprehensive overview of W-based RHEAs for fusion applications and their potential to guide the development and validation of advanced refractory high entropy alloys.

Hatler, Caleb [University of Wisconsin-Madison]↗
DOE OSTI2024

Systematic exploration of the thermochemistry for a set of peroxy hydroperoxy-alkyl radicals

Here, the thermochemistry of peroxy hydroperoxy-alkyl ($\overline{O}$OQOOH) radicals has a significant influence on the reactivity of fuels and on the formation of highly oxygenated molecules (HOMs) in the atmosphere. Theoretical characterization of these radicals can be arduous due to their molecular size and complex fundamental interactions, such as hydrogen-bonding and torsional anharmonicity, and difficult to validate in the absence of any direct experimental thermochemical data. In this work, we systematically explore the thermochemistry of a set of $\overline{O}$OQOOH radicals with five increasingly affordable approaches with considerations for these interactions. In doing so, we present a novel conformer selection approach suited to predict properties at combustion temperatures. As a corollary, we also highlight the shortcomings in the standard choice of the ground conformer as the reference. The set of molecules is comprised of 149 C 2 -C 8 $\overline{O}$OQOOH radicals, selected to encompass a wide variety of branching and substitution patterns. Comparisons amongst the approaches help quantify the errors arising from various simplifying assumptions. For the largest of these radicals, and with the most affordable approach, final predictions of Gibbs energies are assigned a 2 σ uncertainty of 4 kcal mol -1 in the negative temperature coefficient region.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2005

Assume-Guarantee Testing

Verification techniques for component-based systems should ideally be able to predict properties of the assembled system through analysis of individual components before assembly. This work introduces such a modular technique in the context of testing. Assume-guarantee testing relies on the (automated) decomposition of key system-level requirements into local component requirements at design time. Developers can verify the local requirements by checking components in isolation; failed checks may indicate violations of system requirements, while valid traces from different components compose via the assume-guarantee proof rule to potentially provide system coverage. These local requirements also form the foundation of a technique for efficient predictive testing of assembled systems: given a correct system run, this technique can predict violations by alternative system runs without constructing those runs. We discuss the application of our approach to testing a multi-threaded NASA application, where we treat threads as components.

Blundell, Colin↗
DOE OSTI2025

Altermagnetism in the layered intercalated transition metal dichalcogenide CoNb 4 Se 8

Altermagnets (AMs) are a new class of magnetic materials that combine the beneficial spintronics properties of ferromagnets and antiferromagnets, garnering significant attention recently. Here, we have identified altermagnetism in a layered intercalated transition metal diselenide, CoNb 4 Se 8 , which crystallizes with an ordered sublattice of intercalated Co atoms between NbSe 2 layers. Single crystals are synthesized, and the structural characterizations are performed using single crystal diffraction and scanning tunneling microscopy. Magnetic measurements reveal easy-axis antiferromagnetism below 168 K. Density functional theory (DFT) calculations indicate that A-type antiferromagnetic ordering with easy-axis spin direction is the ground state, which is verified through single crystal neutron diffraction experiments. Electronic band structure calculations in this magnetic state display spin-split bands, confirming altermagnetism in this compound. The layered structure of CoNb 4 Se 8 presents a promising platform for testing various predicted properties associated with altermagnetism.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enhancing generative molecular design via uncertainty-guided fine-tuning of variational autoencoders

In recent years, deep generative models have been successfully applied to various molecular design tasks, particularly in the life and materials sciences. One critical challenge for pre-trained generative molecular design (GMD) models is to fine-tune them to be better suited for downstream design tasks that aim at optimizing specific molecular properties. However, redesigning and training an existing effective generative model from scratch for each new design task are impractical. Furthermore, the black-box nature of typical downstream tasks that involve property prediction makes it nontrivial to optimize the generative model in a task-specific manner. In this work, we propose an uncertainty-guided fine-tuning strategy that can effectively enhance a pre-trained variational autoencoder (VAE) for GMD through performance feedback in an active learning setting. The strategy begins by quantifying the model uncertainty of the generative model using an efficient active subspace-based UQ (uncertainty quantification) scheme. Next, the decoder diversity within the characterized model uncertainty class is explored to expand the viable space of molecular generation. The low-dimensionality of the active subspace makes this exploration tractable using a black-box optimization scheme, which in turn enables us to identify and leverage a diverse set of high-performing models to generate enhanced molecules. Empirical results across six target molecular properties using multiple VAE-based generative models demonstrate that our uncertainty-guided fine-tuning strategy consistently leads to improved models that outperform the original pre-trained models.

97 MATHEMATICS AND COMPUTING↗